Demand driven manufacturing and decision making in Fashion

我们正在解决时尚供应链中的需求预测。你可能不知道，服装生产每年的二氧化碳排放量相当于航空业和全球航运业的总和。这意味着什么？15%的衣服，从来没有卖出去。每年有4000亿美元直接被填埋。与此同时，品牌在过早销售的高需求产品上损失了6000亿美元的收入。时尚行业已经崩溃，由于糟糕的需求预测，它每年损失1万亿美元。也就是说，需求预测和供应链优化是一个很难解决的问题-更接近于F1空气动力学，而不是中学数学。然而今天，这是由训练团队完成的，他们几乎没有数学训练。因此，我们只能依靠极其手动和基于直觉的预测。凭直觉，而不是分析。没必要这样。品牌拥有丰富的数据，具有尚未开发的预测能力。作为一个行业，时装业每天记录的数据点数量仅次于制造业。我们的项目旨在建立一个想法，该想法是由我们董事会的两位需求预测教授进行的研究启发的。我们有很强的理论基础相信，通过让品牌在正式发布前向消费者预览（预售）新产品，他们可以将需求预测提高50 - 70%。这使得品牌能够提高高需求产品的价格，以更好地匹配固定供应量与大于预期的需求。提高效率和回报。同时也提醒下游部门从供应链、市场营销和店内/网上销售补充，以促进低需求产品。这使品牌更接近于对需求做出反应，并提高回报，效率和时尚行业的可持续性。

We are solving demand forecasting in the fashion supply chain. You may not know this but clothing production accounts for as much CO2 emissions annually as aviation and global shipping, combined.What does that mean? 15% of clothes created, never get sold. That's $400bn that ends up straight in landfill, every year. And, At the same time, brands lose out on $600bn of revenue on high demand products that prematurely sell out. The fashion industry is broken, and it is losing $1trn annually from poor demand forecasting.That said, demand forecasting and supply chain optimisation is a hard problem to solve - closer to F1 aerodynamics than secondary school maths. However today, it's done by Merchandising teams, who have little to no mathematical training. And as such, fall back on extremely manual, and gut based forecasting. Applying instinct, not analytics.It doesn't have to be like this. Brands sit on a wealth of data with untapped predictive capacity. As an industry, fashion only trails manufacturing in the number of data points recorded each day.Our project is aiming to build on an idea inspired by the research conducted by two professors of demand forecasting that sit on our board. We have strong theoretical grounding to believe that by enabling brands to preview (pre-sell) new products to consumers ahead of the main launch, they can improve demand forecasts by 50-70%.This enables brands to increase price on high demand products to better match fixed supply with larger than expected demand. Improving efficiency and returns. While also alerting downstream departments for replenishment from the supply chain, marketing and in-store/online merchandising to boost low demand products. This is one step closer to enabling brands to react to demand, and improve returns, efficiency and the sustainability of the fashion industry.